Reinforcement Learning for Control of Valves
This work is significant for industrial engineers seeking to apply reinforcement learning to control nonlinear valves, offering a comparison with PID and introducing a simplified learning approach.
This paper explores reinforcement learning (RL) as an optimal-control strategy for nonlinear valves, comparing it against the traditional PID strategy. The RL controller demonstrated superior signal tracking speed and lower error compared to PID, while PID excelled in disturbance rejection.
This paper is a study of reinforcement learning (RL) as an optimal-control strategy for control of nonlinear valves. It is evaluated against the PID (proportional-integral-derivative) strategy, using a unified framework. RL is an autonomous learning mechanism that learns by interacting with its environment. It is gaining increasing attention in the world of control systems as a means of building optimal-controllers for challenging dynamic and nonlinear processes. Published RL research often uses open-source tools (Python and OpenAI Gym environments). We use MATLAB's recently launched (R2019a) Reinforcement Learning Toolbox to develop the valve controller; trained using the DDPG (Deep Deterministic Policy-Gradient) algorithm and Simulink to simulate the nonlinear valve and create the experimental test-bench for evaluation. Simulink allows industrial engineers to quickly adapt and experiment with other systems of their choice. Results indicate that the RL controller is extremely good at tracking the signal with speed and produces a lower error with respect to the reference signal. The PID, however, is better at disturbance rejection and hence provides a longer life for the valves. Successful machine learning involves tuning many hyperparameters requiring significant investment of time and efforts. We introduce "Graded Learning" as a simplified, application oriented adaptation of the more formal and algorithmic "Curriculum for Reinforcement Learning". It is shown via experiments that it helps converge the learning task of complex non-linear real world systems. Finally, experiential learnings gained from this research are corroborated against published research.